Driving Sustainable Transport Infrastructure in Urban Regions of Developing Markets: Leveraging Private Sector Involvement
Bibliographic record
Abstract
The sustainable development of urban transport infrastructure plays a pivotal role in fostering economic growth and modernization, particularly within developing economies.Yet, the financial capacity of public budgets often proves inadequate to satisfy the substantial and longterm capital demands of such projects, rendering private sector participation essential.This study investigates the critical barriers impeding effective collaboration between public and private stakeholders in Vietnam, an illustrative case of developing market dynamics.Adopting a mixed-method qualitative approach grounded in semi-structured interviews and thematic analysis, the research identifies five dominant barriers: demand variability, complexities in land acquisition, constraints in financial access, limited institutional capacity within the public sector, and prolonged administrative approval processes.Comparative insights drawn from regional and international contexts are also used to formulate targeted strategies.The findings contribute to strengthening the theoretical and practical underpinnings of public-private partnership (PPP) frameworks in emerging economies and provide transferable lessons for achieving sustainable and resilient infrastructure delivery across comparable developing contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".